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Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

For fledgling health economists starting in the world of economic evaluation, the NICE reference case is somewhat of a holy text. If in doubt, check the reference case. The concept of a reference case for economic evaluation has been around since the first US Panel on Cost-Effectiveness in Health and Medicine in 1996 and NICE has routinely used its own reference case for well over a decade. The primary purpose of the reference case is to improve the quality and comparability of economic evaluations by standardising methodological practices. There have been arguments made that the same methods are not appropriate for all medical technologies, particularly those in rare diseases or where no treatment currently exists. The focus of this paper is on gene therapy: a novel method that inserts genetic material into cells (as opposed to a drug/surgery) to treat or prevent disease. In this area there has been significant debate as to the appropriateness of the reference case and whether a new reference case is required in this transformative but expensive area. The purpose of the article was to examine the characteristics of gene therapy and make recommendations on changes to the reference case accordingly.

The paper does an excellent job of unpicking the key components of economic evaluation in relation to gene therapy to examine where weaknesses in current reference cases may lie. Rather than recommend that a new reference case be created, they identify specific areas that should be paid special attention when evaluating gene therapy. Additionally, they produce a three part checklist to help analysts to consider what aspects of their economic evaluation they should consider further. For those about to embark on an economic evaluation of a gene therapy intervention, this paper represents an excellent starting point to guide your methodological choices.

The first line of the ‘summary’ section of this paper caught
my eye: “Obesity can spread more easily
if it is not perceived negatively”. This stirred up contradictory thoughts. From a public health standpoint we should be doing our utmost to prevent increasing levels of obesity and their related co-morbidities, whilst simultaneously we should be promoting body positivity and well-being for mental health. Is there a tension here? Might promoting body positivity and well-being enable the spread of obesity? This paper doesn’t really answer that question, instead it sought to investigate whether overweight and obesity had differing effects on mental health within different populations groups.

The study is set in Mexico which has the highest rate of obesity in the world with 70% of the population being overweight or obese. Previous research suggests that obesity spreads more easily if not perceived negatively. This paper hypothesises that this effect will be more acute among the poor and middle classes where obesity is more prevalent. The study aimed to reveal the extent of the impact of obesity on well-being whilst controlling for common determinants of well-being by examining the impact of measures of fatness on subjective well-being, allowing for heterogeneous effects across differing groups. The paper focused only on women, who tend to be more affected by excess weight than men (in Mexico at least).

To assess subjective well-being (SWB) the General Health Questionnaire (GHQ) was used whilst weight status was measured using waist to height ratio and additionally an obesity dummy. Data was sourced from the Mexican Family and Life Survey and the baseline sample included over 13,000 women. Various econometric models were employed ranging from OLS to instrumental variable estimations, details of which can be found within the paper.

The results supported the hypothesis. They found that there was a negative effect of fatness on well-being for the rich, whilst there was a positive effect for the poor. This has interesting policy implications: policy attempt to reduce obesity may not work if excess weight is not perceived to be an issue. The findings in this study imply that different policy measures are likely necessary for intervening in the wealthy and the poor in Mexico. The paper offers several explanations as to why this relationship may exist, ranging from the poor having lower returns from healthy time (nod to the Grossman model), to differing labour market penalties from fatness due to different job types for the rich and the poor.

Obviously there are limits to the generalisability of these findings, however it does raise interesting questions about how we should seek to prevent obesity within different elements of society, and the unintended consequences that shifts in attitudes may have.

Those who follow the methodological side of outcome measurement will be familiar with the capability approach, operationalised by the ICECAP suite of measures amongst others. These measures focus on what people are able to do, rather than what they do. It is now 12-13 years since the first ICECAP measure was developed: the ICECAP-O designed for use in older adults. Given the ICECAP measures are now included within the NICE reference case for the economic evaluation of social care, it is a pertinent time to look back over the past decade to assess whether the ICECAP measures are being used and, if so, to what degree and how. This systematic review focusses on the oldest of the ICECAP measures, the ICECAP-O, and examines whether it has been used, and for what purpose as well as summarising the results from psychometric papers.

An appropriate search strategy was deployed within the usual health economic databases, and the PRISMA checklist was used to guide the review. In total 663 papers were identified, of which 51 papers made it through the screening process.

The first 8 years of the ICECAP-O’s life is characterised by an increasing amount of psychometric studies, however in 2014 a reversal occurred. Simultaneously, the number of studies using the ICECAP-O within economic evaluations has slowly increased, surmounting the number examining the psychometric properties, and has increased year-on-year in the three years up to 2018. Overall, the psychometric literature found the ICECAP-O to have good construct validity and generally good content validity with the occasional exception in groups of people with specific medical needs. Although the capability approach has gained prominence, the studies within the review suggest it is still very much seen as a secondary instrument to the EQ-5D and QALY framework, with results typically being brief with little to no discussion or interpretation of the ICECAP-O results.

One of the key limitations to the ICECAP framework to date relates to how economists and decision makers should use the results from the ICECAP instruments. Should capabilities be combined with time (e.g. years in full capability), or should some minimum (sufficient) capability threshold be used? The paper concludes that in the short term, presenting results in terms of ‘years of full capability’ is the best bet, however future research should focus on identifying sufficient capability and establishing monetary thresholds for a year with sufficient capability. Given this, whilst the ICECAP-O has seen increased use over the years, there is still significant work to be done to facilitate decision making and for it to routinely be used as a primary outcome for economic evaluation.

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Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Childhood obesity is an issue which has risen to prominence in recent years. Concurrently, there has been an increased interest in measuring utility values in children for use in economic evaluation. In the obesity context, there are relatively few studies that have examined whether childhood weight status is associated with preference-based utility and, following, whether such measures are useful for the economic evaluation of childhood obesity interventions. This study sought to tackle this issue using the proxy version of the Health Utilities Index Mark 3 (HUI-3) and weight status data in 368 children aged five years. Associations between weight status and HUI-3 score were assessed using various regression techniques. No statistically significant differences were found between weight status and preference-based health-related quality of life (HRQL). This adds to several recent studies with similar findings which imply that young children may not experience any decrements in HRQL associated with weight status, or that the measures we have cannot capture these decrements. When considering trial-based economic evaluation of childhood obesity interventions, this highlights that we should not be solely relying on preference-based instruments.

For those of us who work on trials, we almost always attempt to do some sort of ‘societal’ perspective incorporating benefits beyond health. When it comes to valuing leisure time and unpaid work there is a dearth of literature and numerous methodological challenges which has led to a bit of a scatter-gun approach to measuring and valuing (usually by ignoring) this time. The authors in the paper sought to value unpaid work (e.g. household chores and voluntary work) and leisure time (“non-productive” time to be spent on one’s likings, nb. this includes lunch breaks). They did this using online questionnaires which included contingent valuation exercises (WTP and WTA) in a sample of representative adults in the Netherlands. Regression techniques following best practice were used (two-part models with transformed data). Using WTA they found an additional hour of unpaid work and leisure time was valued at €16 Euros, whilst the WTP value was €9.50. These values fall into similar ranges to those used in other studies. There are many issues with stated preference studies, which the authors thoroughly acknowledge and address. These costs, so often omitted in economic evaluation, have the potential to be substantial and there remains a need to accurately value this time. Capturing and valuing these time costs remains an important issue, specifically, for those researchers working in countries where national guidelines for economic evaluation prefer a societal perspective.

At the start of every trial, we ask “so what measures should we include?” In the UK, the EQ-5D is the default option, though this decision is not often straightforward. Mental health disorders have a huge burden of impact in terms of both costs (economic and healthcare) and health-related quality of life. How we currently measure the impact of such disorders in economic evaluation often receives scrutiny and there has been recent interest in broadening the evaluative space beyond health to include wellbeing, both subjective wellbeing (SWB) and capability wellbeing (CWB). This study sought to identify which dimensions of HRQL, SWB and CWB were most affected by depression (the most common mental health disorder) and to examine the sensitivity of existing multi-attribute utility instruments (MAUIs) to these dimensions. The study used data from the “Multi-Instrument Comparison” study – this includes lots of measures, including depression measures (Depression Anxiety Stress Scale, Kessler Psychological Distress Scale); SWB measures (Personal Wellbeing Index, Satisfaction with Life Scale, Integrated Household Survey); CWB (ICECAP-A); and multi-attribute utility instruments (15D, AQoL-4D, AQoL-8D, EQ-5D-5L, HUI-3, QWB-SA, and SF-6D). To identify dimensions that were important, the authors used the ‘Glass’s Delta effect size’ (the difference between the mean scores of healthy and self-reported groups divided by the standard deviation of the healthy group). To investigate the extent to which current MAUIs capture these dimensions, each MAUI was regressed on each dimension of HRQL, CWB and SWB. There were lots of interesting findings. Unsurprisingly, the most important dimensions were in the psychosocial dimensions of HRQL (e.g. the ‘coping’, ‘happiness’, and ‘self-worth’ dimensions of the AQoL-8D). Interestingly, the ICECAP-A proved to be the best measure for distinguishing between healthy individuals and those with depression. The SWB measures, on the other hand, were less impacted by depression. Of the MAUIs, the AQoL-8D was the most sensitive, whilst our beloved EQ-5D-5L and SF-6D were the least sensitive at distinguishing dimensions. There is a huge amount to unpack within this study, but it does raise interesting questions regarding measurement issues and the impact of broadening the evaluative space for decision makers. Finally, it’s worth noting that a new MAUI (ReQoL) for mental health has been recently developed – although further testing is needed, this is something to consider in future.

Like this:

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Sugar taxes are one of the public health policy options currently in vogue. Countries including Mexico, the UK, South Africa, and Sri Lanka all have sugar taxes. The aim of such levies is to reduce demand for the most sugary drinks, or if the tax is absorbed on the supply side, which is rare, to encourage producers to reduce the sugar content of their drinks. One may also view it as a form of Pigouvian taxation to internalise the public health costs associated with obesity. Chile has long had an ad valorem tax on soft drinks fixed at 13%, but in 2014 decided to pursue a sugar tax approach. Drinks with more than 6.25g/100ml saw their tax rate rise to 18% and the tax on those below this threshold dropped to 10%. To understand what effect this change had, we would want to know three key things along the causal pathway from tax policy to sugar consumption: did people know about the tax change, did prices change, and did consumption behaviour change. On this latter point, we can consider both the overall volume of soft drinks and whether people substituted low sugar for high sugar beverages. Using the Kantar Worldpanel, a household panel survey of purchasing behaviour, this paper examines these questions.

Everyone in Chile was affected by the tax so there is no control group. We must rely on time series variation to identify the effect of the tax. Sometimes, looking at plots of the data reveals a clear step-change when an intervention is introduced (e.g. the plot in this post), not so in this paper. We therefore rely heavily on the results of the model for our inferences, and I have a couple of small gripes with it. First, the model captures household fixed effects, but no consideration is given to dynamic effects. Some households may be more or less likely to buy drinks, but their decisions are also likely to be affected by how much they’ve recently bought. Similarly, the errors may be correlated over time. Ignoring dynamic effects can lead to large biases. Second, the authors choose among different functional form specifications of time using Akaike Information Criterion (AIC). While AIC and the Bayesian Information Criterion (BIC) are often thought to be interchangeable, they are not; AIC estimates predictive performance on future data, while BIC estimates goodness of fit to the data. Thus, I would think BIC would be more appropriate. Additional results show the estimates are very sensitive to the choice of functional form by an order of magnitude and in sign. The authors estimate a fairly substantial decrease of around 22% in the volume of high sugar drinks purchased, but find evidence that the price paid changed very little (~1.5%) and there was little change in other drinks. While the analysis is generally careful and well thought out, I am not wholly convinced by the authors’ conclusions that “Our main estimates suggest a significant, sizeable reduction in the volume of high-tax soft drinks purchased.”

Missing data is a ubiquitous problem. I’ve never used a data set where no observations were missing and I doubt I’m alone. Despite its pervasiveness, it’s often only afforded an acknowledgement in the discussion or perhaps, in more complete analyses, something like multiple imputation will be used. Indeed, the majority of trials in the top medical journals don’t handle it correctly, if at all. The majority of the methods used for missing data in practice assume the data are ‘missing at random’ (MAR). One interpretation is that this means that, conditional on the observable variables, the probability of data being missing is independent of unobserved factors influencing the outcome. Another interpretation is that the distribution of the potentially missing data does not depend on whether they are actually missing. This interpretation comes from factorising the joint distribution of the outcome and an indicator of whether the datum is observed , along with some covariates , into a conditional and marginal model: , a so-called pattern mixture model. This contrasts with the ‘selection model’ approach: .

This paper considers a Bayesian approach using the pattern mixture model for missing data for health economic evaluation. Specifically, the authors specify a multivariate normal model for the data with an additional term in the mean if it is missing, i.e. the model of . A model is not specified for . If it were then you would typically allow for correlation between the errors in this model and the main outcomes model. But, one could view the additional term in the outcomes model as some function of the error from the observation model somewhat akin to a control function. Instead, this article uses expert elicitation methods to generate a prior distribution for the unobserved terms in the outcomes model. While this is certainly a legitimate way forward in my eyes, I do wonder how specification of a full observation model would affect the results. The approach of this article is useful and they show that it works, and I don’t want to detract from that but, given the lack of literature on missing data in this area, I am curious to compare approaches including selection models. You could even add shared parameter models as an alternative, all of which are feasible. Perhaps an idea for a follow-up study. As a final point, the models run in WinBUGS, but regular readers will know I think Stan is the future for estimating Bayesian models, especially in light of the problems with MCMC we’ve discussed previously. So equivalent Stan code would have been a bonus.

This is an economics blog. But focusing solely on economics papers in these round-ups would mean missing out on some papers from related fields that may provide insight into our own work. Thus I present to you a politics and sociology paper. It is not my field and I can’t give a reliable appraisal of the methods, but the results are of interest. In the global fight against non-communicable diseases, there is a range of policy tools available to governments, including the sugar tax of the paper at the top. The WHO recommends a large number. However, there is ongoing debate about whether trade rules and agreements are used to undermine this public health legislation. One agreement, the Technical Barriers to Trade (TBT) Agreement that World Trade Organization (WTO) members all sign, states that members may not impose ‘unnecessary trade costs’ or barriers to trade, especially if the intended aim of the measure can be achieved without doing so. For example, Philip Morris cited a bilateral trade agreement when it sued the Australian government for introducing plain packaging claiming it violated the terms of trade. Philip Morris eventually lost but not after substantial costs were incurred. In another example, the Thai government were deterred from introducing a traffic light warning system for food after threats of a trade dispute from the US, which cited WTO rules. However, there was no clear evidence on the extent to which trade disputes have undermined public health measures.

This article presents results from a new database of all TBT WTO challenges. Between 1995 and 2016, 93 challenges were raised concerning food, beverage, and tobacco products, the number per year growing over time. The most frequent challenges were over labelling products and then restricted ingredients. The paper presents four case studies, including Indonesia delaying food labelling of fat, sugar, and salt after challenge by several members including the EU, and many members including the EU again and the US objecting to the size and colour of a red STOP sign that Chile wanted to put on products containing high sugar, fat, and salt.

We have previously discussed the politics and political economy around public health policy relating to e-cigarettes, among other things. Understanding the political economy of public health and phenomena like government failure can be as important as understanding markets and market failure in designing effective interventions.